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Research • Center of Excellence SAFE • Quarter 1/2014<br />
I targeted our research on banks, insurers, hedge<br />
funds and brokers operating in the U.S. market.<br />
We proposed several econometric measures of<br />
connectedness based on principal component<br />
analysis and Granger causality networks applied<br />
to the monthly returns of the target institutions.<br />
The aim of our research was to capture the network<br />
of causal relationships between the largest<br />
financial institutions across the four types of institutions<br />
mentioned. Moreover, we tried to understand<br />
whether these measures of connectedness<br />
are meaningful indicators of the condition<br />
of the financial markets and to discover, in the<br />
light of the last financial crisis, which are the key<br />
banks, insurers, hedge funds and brokers as far as<br />
systemic risk is concerned.<br />
The empirical analysis conducted shows that the<br />
degree of connectedness increases before and during<br />
crises, and the pattern of the last financial crisis<br />
suggests that banks are more central to systemic<br />
risk than the so-called “shadow banking system”.<br />
Moreover, the proposed measures are able to reflect<br />
periods of market dislocation and distress<br />
with promising out-of-sample characteristics.<br />
“Sovereign, bank and insurance credit spreads:<br />
connectedness and system networks”<br />
As banking and insurance system risk has become<br />
an important element in the determination of sovereign<br />
risk and vice-versa, in this article (Billio et al.,<br />
Figure 1: Network diagrams of linear Granger-causality relationships that are statistically significant<br />
at the 1% level between banks (red), insurers (black), and sovereigns (blue) – from July 2004 to June<br />
2007 (left) and from April 2009 to March 2012 (right)<br />
2013) we focused on the relationship between the The application of the Granger causality test to ELR<br />
private financial sector and sovereign risk, aiming data allowed us to ascertain the evolution of connectedness.<br />
An empirical analysis showed that the<br />
to highlight the connectedness among financial<br />
institutions and sovereigns. To that extent, we applied<br />
several econometric measures of connect-<br />
in our sample is highly connected in a very dynamic<br />
system of banks, insurers and sovereigns included<br />
edness based on Granger causality networks to way. These interconnections are not symmetric. An<br />
changes in the sovereign risk of European countries analysis of the number of connections points to a<br />
and the credit risk of major European, U.S. and Japanese<br />
banks, broker-dealers and insurers. Financial stitutions after the crisis than before (see Figure 1).<br />
much greater connectedness among financial in-<br />
institution and sovereign risk is measured using Expected<br />
Loss Ratio (ELR) data from the Moody’s KMV and sovereigns were much more connected during<br />
Moreover, time series showed that banks, insurers<br />
database. This uses equity, equity volatility and the and after the global financial crisis than before. In<br />
default barrier (from accounting information) to conclusion, the proposed financial network measures<br />
can be utilized as early warning indicators<br />
get the “distance-to-distress” which it then maps<br />
to a default probability using a pool of 30 years of and to assess the complexity of the financial system.<br />
This framework can also be employed for default information.<br />
the<br />
analysis of shocks, spillovers and tradeoffs with regard<br />
to different policy alternatives.<br />
References<br />
Billio, M., Getmansky, M., Gray, D., Lo, A. W.,<br />
Merton, R. C., Pelizzon, R. (2013)<br />
“Sovereign, Bank and Insurance Credit Spreads:<br />
Connectedness and System Networks”,<br />
MIT Working Paper.<br />
Billio, M., Getmansky, M., Gray, D., Lo, A. W.,<br />
Pelizzon, R. (2012)<br />
“Econometric measures of connectedness and systemic<br />
risk in the financial and insurance sectors”,<br />
Journal of Financial Economics, Vol. 104, Issue 3,<br />
pp. 535-559.<br />
De Bandt, O., Hartmann, P. (2000)<br />
“Systemic Risk: A Survey”,<br />
European Central Bank Working Paper Series, No. 35.<br />
Merton, R. C., Billio, M., Getmansky, M., Gray, D.,<br />
Lo, A. W., Pelizzon, R (2013)<br />
“On a New Approach for Analyzing and Managing<br />
Macrofinancial Risks”,<br />
Financial Analysts Journal, Vol. 69, Issue 2.<br />
Merton, R. C. (1974)<br />
“On the Pricing of Corporate Debt: The Risk<br />
Structure of Interest Rates”,<br />
Journal of Finance, Vol. 29, pp. 449-470.<br />
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